Luchinsky D G, Millonas M M, Smelyanskiy V N, Pershakova A, Stefanovska A, McClintock P V E
Newstead Mission Critical Technologies, Inc., 9100 Wilshire Boulevard, Suite 540, East Beverly Hills, California 90212-3437, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Aug;72(2 Pt 1):021905. doi: 10.1103/PhysRevE.72.021905. Epub 2005 Aug 19.
We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.
我们提出了一种贝叶斯动态推理方法，用于通过从血压时间序列数据进行逆建模来表征人类的心肺（CR）动态。该技术适用于广泛的随机动态模型，并且可以在没有严格计算要求的情况下实现。我们发现了一个简单的非线性动态模型，该模型描述了CR动态主要频段中的实测血压时间序列。使用参数接近实验中推断参数的模型生成数据来研究该方法的准确性。讨论了推断模型与著名的压力反射逐搏模型之间的联系。
